{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vitaldb/examples/blob/master/vitaldb_python_library.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2COxMJNUxIh"
      },
      "source": [
        "# VitalDB Python Library\n",
        "In this example, we will learn how to handle Vital files using `vitaldb` Python library."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0pAlf-93XLo9"
      },
      "source": [
        "## Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yz6rZEtHVQwG"
      },
      "source": [
        "### Install 'vitaldb' library\n",
        "Intall `vitaldb` library in order to read Vital files."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t88MHi_hcHAT",
        "outputId": "436dbecc-a639-4ff3-a1ea-27608fa04ff9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting vitaldb\n",
            "  Downloading vitaldb-1.3.3-py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.6/54.6 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from vitaldb) (1.22.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from vitaldb) (1.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from vitaldb) (2.27.1)\n",
            "Collecting wfdb (from vitaldb)\n",
            "  Downloading wfdb-4.1.1-py3-none-any.whl (159 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m159.9/159.9 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->vitaldb) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->vitaldb) (2022.7.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->vitaldb) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->vitaldb) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->vitaldb) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->vitaldb) (3.4)\n",
            "Requirement already satisfied: SoundFile>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from wfdb->vitaldb) (0.12.1)\n",
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.10/dist-packages (from wfdb->vitaldb) (3.7.1)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wfdb->vitaldb) (1.10.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb->vitaldb) (1.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb->vitaldb) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb->vitaldb) (4.39.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb->vitaldb) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb->vitaldb) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb->vitaldb) (8.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb->vitaldb) (3.0.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->vitaldb) (1.16.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from SoundFile>=0.10.0->wfdb->vitaldb) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->SoundFile>=0.10.0->wfdb->vitaldb) (2.21)\n",
            "Installing collected packages: wfdb, vitaldb\n",
            "Successfully installed vitaldb-1.3.3 wfdb-4.1.1\n"
          ]
        }
      ],
      "source": [
        "!pip install vitaldb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hI2a3YxAfzME"
      },
      "source": [
        "###Download sample vital files\n",
        "Download sample vital files from the VitalDB website using the command below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jK_-H6WtUz_9",
        "outputId": "66d752cd-a5f7-43d0-f5af-a55994363eff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-06-07 07:32:18--  https://vitaldb.net/samples.zip\n",
            "Resolving vitaldb.net (vitaldb.net)... 15.197.255.47\n",
            "Connecting to vitaldb.net (vitaldb.net)|15.197.255.47|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 104335514 (100M) [application/zip]\n",
            "Saving to: ‘samples.zip’\n",
            "\n",
            "samples.zip         100%[===================>]  99.50M  29.8MB/s    in 4.2s    \n",
            "\n",
            "2023-06-07 07:32:23 (23.8 MB/s) - ‘samples.zip’ saved [104335514/104335514]\n",
            "\n",
            "Archive:  samples.zip\n",
            "  inflating: 1-1.vital               \n",
            "  inflating: 1-2.vital               \n",
            "  inflating: 1.vital                 \n",
            "  inflating: 2.vital                 \n",
            "  inflating: 3.vital                 \n",
            "  inflating: 4.vital                 \n"
          ]
        }
      ],
      "source": [
        "!wget https://vitaldb.net/samples.zip\n",
        "!unzip samples.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmgInduClfAR"
      },
      "source": [
        "## Vital File API\n",
        "The Vital File API provides easy functions (`vital_trks`, `vital_recs`) and class (`VitalFile`) for reading a track list and contents from a vital file."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVniWCJAU7Wj"
      },
      "source": [
        "### Read the track list from a vital file\n",
        "You can read the track list from a vital file using `vital_trks` function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oh_54t5FZkYM",
        "outputId": "2f270869-d780-446b-9a26-bddf722622c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SNUADC/ART',\n",
              " 'SNUADC/ECG_II',\n",
              " 'SNUADC/ECG_V5',\n",
              " 'SNUADC/PLETH',\n",
              " 'Solar8000/HR',\n",
              " 'Solar8000/ST_I',\n",
              " 'Solar8000/ST_II',\n",
              " 'Solar8000/ST_III',\n",
              " 'Solar8000/ST_AVL',\n",
              " 'Solar8000/ST_AVR',\n",
              " 'Solar8000/ST_AVF',\n",
              " 'Solar8000/ART_MBP',\n",
              " 'Solar8000/ART_SBP',\n",
              " 'Solar8000/ART_DBP',\n",
              " 'Solar8000/PLETH_SPO2',\n",
              " 'Solar8000/PLETH_HR',\n",
              " 'Solar8000/BT',\n",
              " 'Solar8000/VENT_MAWP',\n",
              " 'Solar8000/ST_V5',\n",
              " 'Solar8000/NIBP_MBP',\n",
              " 'Solar8000/NIBP_SBP',\n",
              " 'Solar8000/NIBP_DBP',\n",
              " 'Solar8000/VENT_PIP',\n",
              " 'Solar8000/VENT_RR',\n",
              " 'Solar8000/VENT_MV',\n",
              " 'Solar8000/VENT_TV',\n",
              " 'Solar8000/VENT_PPLAT',\n",
              " 'Solar8000/GAS2_AGENT',\n",
              " 'Solar8000/GAS2_EXPIRED',\n",
              " 'Solar8000/GAS2_INSPIRED',\n",
              " 'Solar8000/ETCO2',\n",
              " 'Solar8000/INCO2',\n",
              " 'Solar8000/RR_CO2',\n",
              " 'Solar8000/FEO2',\n",
              " 'Solar8000/FIO2',\n",
              " 'Solar8000/VENT_INSP_TM',\n",
              " 'Solar8000/VENT_SET_TV',\n",
              " 'Solar8000/VENT_SET_PCP',\n",
              " 'Primus/CO2',\n",
              " 'Primus/AWP',\n",
              " 'Primus/INSP_SEVO',\n",
              " 'Primus/EXP_SEVO',\n",
              " 'Primus/PAMB_MBAR',\n",
              " 'Primus/MAWP_MBAR',\n",
              " 'Primus/MAC',\n",
              " 'Primus/VENT_LEAK',\n",
              " 'Primus/INCO2',\n",
              " 'Primus/ETCO2',\n",
              " 'Primus/FEO2',\n",
              " 'Primus/FIO2',\n",
              " 'Primus/FIN2O',\n",
              " 'Primus/FEN2O',\n",
              " 'Primus/SET_FIO2',\n",
              " 'Primus/SET_FRESH_FLOW',\n",
              " 'Primus/SET_AGE',\n",
              " 'Primus/PIP_MBAR',\n",
              " 'Primus/COMPLIANCE',\n",
              " 'Primus/PPLAT_MBAR',\n",
              " 'Primus/PEEP_MBAR',\n",
              " 'Primus/TV',\n",
              " 'Primus/MV',\n",
              " 'Primus/RR_CO2',\n",
              " 'Primus/SET_TV_L',\n",
              " 'Primus/SET_INSP_TM',\n",
              " 'Primus/SET_RR_IPPV',\n",
              " 'Primus/SET_INTER_PEEP',\n",
              " 'Primus/SET_PIP',\n",
              " 'Primus/SET_INSP_PAUSE',\n",
              " 'Primus/INSP_DES',\n",
              " 'Primus/EXP_DES',\n",
              " 'BIS/EEG1_WAV',\n",
              " 'BIS/EEG2_WAV',\n",
              " 'BIS/BIS',\n",
              " 'BIS/SQI',\n",
              " 'BIS/EMG',\n",
              " 'BIS/SR',\n",
              " 'BIS/SEF',\n",
              " 'BIS/TOTPOW']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import vitaldb\n",
        "trks = vitaldb.vital_trks('1.vital')\n",
        "trks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XARQ3Zs1WVE7"
      },
      "source": [
        "### Read the samples in a vital file\n",
        "You can read the samples(values) in a vital file using `vital_recs` function. It returns the samples in a numpy array."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "qsQ-vSMpUjp5",
        "outputId": "3e4d5fce-34a1-4a69-ddea-b188cacb2449"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0.8993597   -7.822235  ]\n",
            " [  0.83023787  -8.809692  ]\n",
            " [  0.7808652   -7.822235  ]\n",
            " ...\n",
            " [ -0.09797144 -20.65918   ]\n",
            " [ -0.06834793  32.663483  ]\n",
            " [  0.45500422  -4.8598633 ]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "vals = vitaldb.vital_recs('1.vital', ['ECG_II','ART'], 1/100)\n",
        "print(vals)\n",
        "\n",
        "ecg = vals[:,0]\n",
        "art = vals[:,1]\n",
        "\n",
        "# plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.subplot(211)\n",
        "plt.plot(ecg[110000:111000], color='g')\n",
        "plt.subplot(212)\n",
        "plt.plot(art[110000:111000], color='r')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwgzMGYvXyIZ"
      },
      "source": [
        "### Create VitalFile object\n",
        "`VitalFile` class helps you read, write, and edit vital file easily and seamlessly. You can create `VitalFile` object by opening one or list of local or online vital file(s)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-769iObcIms",
        "outputId": "22c2a345-090d-4a78-e335-8cbb69cb4e99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "duration 11541.150 seconds 78 tracks\n"
          ]
        }
      ],
      "source": [
        "vf = vitaldb.VitalFile('1.vital')\n",
        "print(f'duration {vf.dtend - vf.dtstart:.3f} seconds {len(vf.trks)} tracks')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aq38cDeobuKO"
      },
      "source": [
        "You can even directly read the vital file online."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5Fyv9l8biP6",
        "outputId": "7561e040-fc9c-4a0d-b406-ce5aafabad93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "duration 11541.150 seconds 78 tracks\n"
          ]
        }
      ],
      "source": [
        "vf = vitaldb.VitalFile('https://api.vitaldb.net/0001.vital')\n",
        "print(f'duration {vf.dtend - vf.dtstart:.3f} seconds {len(vf.trks)} tracks')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBz7vEL2bNb7"
      },
      "source": [
        "The code below create `VitalFile` object by merging two vital files.\n",
        "\n",
        "> Note: Only vital files within 2 days can be merged into one file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZmvNSBXaJ_e",
        "outputId": "82d20e95-5da1-462f-9aeb-5492281a6ee3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1-1 duration 3908.135 seconds 79 tracks\n",
            "1-2 duration 6621.162 seconds 79 tracks\n",
            "merged duration 11542.097 seconds 79 tracks\n"
          ]
        }
      ],
      "source": [
        "vf1 = vitaldb.VitalFile('1-1.vital')\n",
        "print(f'1-1 duration {vf1.dtend - vf1.dtstart:.3f} seconds {len(vf1.trks)} tracks')\n",
        "\n",
        "vf2 = vitaldb.VitalFile('1-2.vital')\n",
        "print(f'1-2 duration {vf2.dtend - vf2.dtstart:.3f} seconds {len(vf2.trks)} tracks')\n",
        "\n",
        "vf = vitaldb.VitalFile(['1-1.vital', '1-2.vital'])  # merged\n",
        "print(f'merged duration {vf.dtend - vf.dtstart:.3f} seconds {len(vf.trks)} tracks')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjPFDoI9fM4D"
      },
      "source": [
        "### Read information from the VitalFile object"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NH19cpsdB8w"
      },
      "source": [
        "All information such as track list and measured samples are in the `vf` object. For example, you can get a list of track names using the `vf.get_track_names()` method or `vf.trks.keys()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5EVoYx14dBCP",
        "outputId": "c6b27427-aae0-4a72-de84-af3b8312b295"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['SNUADC/ART', 'SNUADC/ECG_II', 'SNUADC/ECG_V5', 'SNUADC/PLETH', 'Primus/CO2', 'Primus/AWP', 'Primus/INSP_SEVO', 'Primus/EXP_SEVO', 'Primus/PAMB_MBAR', 'Primus/MAWP_MBAR', 'Primus/MAC', 'Primus/VENT_LEAK', 'Primus/INCO2', 'Primus/ETCO2', 'Primus/FEO2', 'Primus/FIO2', 'Primus/FIN2O', 'Primus/FEN2O', 'Primus/SET_FIO2', 'Primus/SET_FRESH_FLOW', 'Primus/SET_AGE', 'Primus/PIP_MBAR', 'Primus/COMPLIANCE', 'Primus/PPLAT_MBAR', 'Primus/PEEP_MBAR', 'Primus/TV', 'Primus/MV', 'Primus/RR_CO2', 'Primus/SET_TV_L', 'Primus/SET_INSP_TM', 'Primus/SET_RR_IPPV', 'Primus/SET_INTER_PEEP', 'Primus/SET_PIP', 'Primus/SET_INSP_PAUSE', 'Primus/INSP_DES', 'Primus/EXP_DES', 'BIS/EEG1_WAV', 'BIS/EEG2_WAV', 'BIS/BIS', 'BIS/SQI', 'BIS/EMG', 'BIS/SR', 'BIS/SEF', 'BIS/TOTPOW', 'Solar8000/HR', 'Solar8000/ST_I', 'Solar8000/ST_II', 'Solar8000/ST_III', 'Solar8000/ST_AVL', 'Solar8000/ST_AVR', 'Solar8000/ST_AVF', 'Solar8000/ART_MBP', 'Solar8000/ART_SBP', 'Solar8000/ART_DBP', 'Solar8000/PLETH_SPO2', 'Solar8000/PLETH_HR', 'Solar8000/BT', 'Solar8000/VENT_MAWP', 'Solar8000/ST_V5', 'Solar8000/NIBP_MBP', 'Solar8000/NIBP_SBP', 'Solar8000/NIBP_DBP', 'Solar8000/VENT_PIP', 'Solar8000/VENT_RR', 'Solar8000/VENT_MV', 'Solar8000/VENT_TV', 'Solar8000/VENT_PPLAT', 'Solar8000/GAS2_AGENT', 'Solar8000/GAS2_EXPIRED', 'Solar8000/GAS2_INSPIRED', 'Solar8000/ETCO2', 'Solar8000/INCO2', 'Solar8000/RR_CO2', 'Solar8000/FEO2', 'Solar8000/FIO2', 'Solar8000/VENT_INSP_TM', 'Solar8000/VENT_SET_TV', 'Solar8000/VENT_SET_PCP', 'EVENT']\n",
            "dict_keys(['SNUADC/ART', 'SNUADC/ECG_II', 'SNUADC/ECG_V5', 'SNUADC/PLETH', 'Primus/CO2', 'Primus/AWP', 'Primus/INSP_SEVO', 'Primus/EXP_SEVO', 'Primus/PAMB_MBAR', 'Primus/MAWP_MBAR', 'Primus/MAC', 'Primus/VENT_LEAK', 'Primus/INCO2', 'Primus/ETCO2', 'Primus/FEO2', 'Primus/FIO2', 'Primus/FIN2O', 'Primus/FEN2O', 'Primus/SET_FIO2', 'Primus/SET_FRESH_FLOW', 'Primus/SET_AGE', 'Primus/PIP_MBAR', 'Primus/COMPLIANCE', 'Primus/PPLAT_MBAR', 'Primus/PEEP_MBAR', 'Primus/TV', 'Primus/MV', 'Primus/RR_CO2', 'Primus/SET_TV_L', 'Primus/SET_INSP_TM', 'Primus/SET_RR_IPPV', 'Primus/SET_INTER_PEEP', 'Primus/SET_PIP', 'Primus/SET_INSP_PAUSE', 'Primus/INSP_DES', 'Primus/EXP_DES', 'BIS/EEG1_WAV', 'BIS/EEG2_WAV', 'BIS/BIS', 'BIS/SQI', 'BIS/EMG', 'BIS/SR', 'BIS/SEF', 'BIS/TOTPOW', 'Solar8000/HR', 'Solar8000/ST_I', 'Solar8000/ST_II', 'Solar8000/ST_III', 'Solar8000/ST_AVL', 'Solar8000/ST_AVR', 'Solar8000/ST_AVF', 'Solar8000/ART_MBP', 'Solar8000/ART_SBP', 'Solar8000/ART_DBP', 'Solar8000/PLETH_SPO2', 'Solar8000/PLETH_HR', 'Solar8000/BT', 'Solar8000/VENT_MAWP', 'Solar8000/ST_V5', 'Solar8000/NIBP_MBP', 'Solar8000/NIBP_SBP', 'Solar8000/NIBP_DBP', 'Solar8000/VENT_PIP', 'Solar8000/VENT_RR', 'Solar8000/VENT_MV', 'Solar8000/VENT_TV', 'Solar8000/VENT_PPLAT', 'Solar8000/GAS2_AGENT', 'Solar8000/GAS2_EXPIRED', 'Solar8000/GAS2_INSPIRED', 'Solar8000/ETCO2', 'Solar8000/INCO2', 'Solar8000/RR_CO2', 'Solar8000/FEO2', 'Solar8000/FIO2', 'Solar8000/VENT_INSP_TM', 'Solar8000/VENT_SET_TV', 'Solar8000/VENT_SET_PCP', 'EVENT'])\n"
          ]
        }
      ],
      "source": [
        "print(vf.get_track_names())\n",
        "print(vf.trks.keys())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are tracks in VitalFile which contains records."
      ],
      "metadata": {
        "id": "9Vj56HVTnagE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(vf.trks)\n",
        "trk = vf.trks['SNUADC/ART']\n",
        "print(f'type: {trk.type} (1:wav, 2:num, 5:str)')  # track type\n",
        "print(f'name: {trk.name}')  # name\n",
        "print(f'device name: {trk.dname}')  # device name\n",
        "print(f'sample rate: {trk.srate}')  # sample rate\n",
        "print(f'unit: {trk.unit}')  # physical unit\n",
        "print(f'mindisp: {trk.mindisp}')  # minimum value of display\n",
        "print(f'maxdisp: {trk.maxdisp}')  # minimum value of display\n",
        "print(f'color: {trk.col}')  # color (r*255*255 + g*255 + b)\n",
        "print(trk.recs[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jartc7fTnoLX",
        "outputId": "7fcdf4a5-af1b-48ad-abe3-518fe7c148f2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'SNUADC/ART': <vitaldb.utils.Track object at 0x7f23c4e97580>, 'SNUADC/ECG_II': <vitaldb.utils.Track object at 0x7f23c4e97550>, 'SNUADC/ECG_V5': <vitaldb.utils.Track object at 0x7f23fe4bb070>, 'SNUADC/PLETH': <vitaldb.utils.Track object at 0x7f23c2a53ee0>, 'Primus/CO2': <vitaldb.utils.Track object at 0x7f23c4e955d0>, 'Primus/AWP': <vitaldb.utils.Track object at 0x7f23c4e95570>, 'Primus/INSP_SEVO': <vitaldb.utils.Track object at 0x7f23c4e97520>, 'Primus/EXP_SEVO': <vitaldb.utils.Track object at 0x7f23c4e95600>, 'Primus/PAMB_MBAR': <vitaldb.utils.Track object at 0x7f23c4e955a0>, 'Primus/MAWP_MBAR': <vitaldb.utils.Track object at 0x7f23c4e95540>, 'Primus/MAC': <vitaldb.utils.Track object at 0x7f23c4e95510>, 'Primus/VENT_LEAK': <vitaldb.utils.Track object at 0x7f23c4e954b0>, 'Primus/INCO2': <vitaldb.utils.Track object at 0x7f23c4e954e0>, 'Primus/ETCO2': <vitaldb.utils.Track object at 0x7f23c4e95480>, 'Primus/FEO2': <vitaldb.utils.Track object at 0x7f23c4e95450>, 'Primus/FIO2': <vitaldb.utils.Track object at 0x7f23c4e95420>, 'Primus/FIN2O': <vitaldb.utils.Track object at 0x7f23c4e953f0>, 'Primus/FEN2O': <vitaldb.utils.Track object at 0x7f23c4e953c0>, 'Primus/SET_FIO2': <vitaldb.utils.Track object at 0x7f23c4e95390>, 'Primus/SET_FRESH_FLOW': <vitaldb.utils.Track object at 0x7f23c4e95360>, 'Primus/SET_AGE': <vitaldb.utils.Track object at 0x7f23c4e95330>, 'Primus/PIP_MBAR': <vitaldb.utils.Track object at 0x7f23c4e95300>, 'Primus/COMPLIANCE': <vitaldb.utils.Track object at 0x7f23c4e952d0>, 'Primus/PPLAT_MBAR': <vitaldb.utils.Track object at 0x7f23c4e952a0>, 'Primus/PEEP_MBAR': <vitaldb.utils.Track object at 0x7f23c4e95270>, 'Primus/TV': <vitaldb.utils.Track object at 0x7f23c4e95240>, 'Primus/MV': <vitaldb.utils.Track object at 0x7f23c4e95210>, 'Primus/RR_CO2': <vitaldb.utils.Track object at 0x7f23c4e951e0>, 'Primus/SET_TV_L': <vitaldb.utils.Track object at 0x7f23c4e951b0>, 'Primus/SET_INSP_TM': <vitaldb.utils.Track object at 0x7f23c4e95180>, 'Primus/SET_RR_IPPV': <vitaldb.utils.Track object at 0x7f23c4e95150>, 'Primus/SET_INTER_PEEP': <vitaldb.utils.Track object at 0x7f23c4e95120>, 'Primus/SET_PIP': <vitaldb.utils.Track object at 0x7f23c4e950c0>, 'Primus/SET_INSP_PAUSE': <vitaldb.utils.Track object at 0x7f23c4e950f0>, 'Primus/INSP_DES': <vitaldb.utils.Track object at 0x7f23c4e95090>, 'Primus/EXP_DES': <vitaldb.utils.Track object at 0x7f23c4e95060>, 'BIS/EEG1_WAV': <vitaldb.utils.Track object at 0x7f23c4e94f10>, 'BIS/EEG2_WAV': <vitaldb.utils.Track object at 0x7f23c4e94eb0>, 'BIS/BIS': <vitaldb.utils.Track object at 0x7f23c4e94070>, 'BIS/SQI': <vitaldb.utils.Track object at 0x7f23c4e94ee0>, 'BIS/EMG': <vitaldb.utils.Track object at 0x7f23c4e94760>, 'BIS/SR': <vitaldb.utils.Track object at 0x7f23c4e94e80>, 'BIS/SEF': <vitaldb.utils.Track object at 0x7f23c4e94e50>, 'BIS/TOTPOW': <vitaldb.utils.Track object at 0x7f23c4e94e20>, 'Solar8000/HR': <vitaldb.utils.Track object at 0x7f23c4e94d30>, 'Solar8000/ST_I': <vitaldb.utils.Track object at 0x7f23c4e94d00>, 'Solar8000/ST_II': <vitaldb.utils.Track object at 0x7f23c4e94cd0>, 'Solar8000/ST_III': <vitaldb.utils.Track object at 0x7f23c4e94ca0>, 'Solar8000/ST_AVL': <vitaldb.utils.Track object at 0x7f23c4e94c70>, 'Solar8000/ST_AVR': <vitaldb.utils.Track object at 0x7f23c4e94c40>, 'Solar8000/ST_AVF': <vitaldb.utils.Track object at 0x7f23c4e94c10>, 'Solar8000/ART_MBP': <vitaldb.utils.Track object at 0x7f23c4e94be0>, 'Solar8000/ART_SBP': <vitaldb.utils.Track object at 0x7f23c4e94bb0>, 'Solar8000/ART_DBP': <vitaldb.utils.Track object at 0x7f23c4e94b80>, 'Solar8000/PLETH_SPO2': <vitaldb.utils.Track object at 0x7f23c4e94b50>, 'Solar8000/PLETH_HR': <vitaldb.utils.Track object at 0x7f23c4e94b20>, 'Solar8000/BT': <vitaldb.utils.Track object at 0x7f23c4e94af0>, 'Solar8000/VENT_MAWP': <vitaldb.utils.Track object at 0x7f23c4e94ac0>, 'Solar8000/ST_V5': <vitaldb.utils.Track object at 0x7f23c4e94a90>, 'Solar8000/NIBP_MBP': <vitaldb.utils.Track object at 0x7f23c4e94a60>, 'Solar8000/NIBP_SBP': <vitaldb.utils.Track object at 0x7f23c4e94a30>, 'Solar8000/NIBP_DBP': <vitaldb.utils.Track object at 0x7f23c4e94a00>, 'Solar8000/VENT_PIP': <vitaldb.utils.Track object at 0x7f23c4e949d0>, 'Solar8000/VENT_RR': <vitaldb.utils.Track object at 0x7f23c707d120>, 'Solar8000/VENT_MV': <vitaldb.utils.Track object at 0x7f23c4e949a0>, 'Solar8000/VENT_TV': <vitaldb.utils.Track object at 0x7f23c4e95630>, 'Solar8000/VENT_PPLAT': <vitaldb.utils.Track object at 0x7f23c4e95660>, 'Solar8000/GAS2_AGENT': <vitaldb.utils.Track object at 0x7f23c4e94940>, 'Solar8000/GAS2_EXPIRED': <vitaldb.utils.Track object at 0x7f23c4e94970>, 'Solar8000/GAS2_INSPIRED': <vitaldb.utils.Track object at 0x7f23c4e94910>, 'Solar8000/ETCO2': <vitaldb.utils.Track object at 0x7f23c4e946d0>, 'Solar8000/INCO2': <vitaldb.utils.Track object at 0x7f23c4e94850>, 'Solar8000/RR_CO2': <vitaldb.utils.Track object at 0x7f23c4e948b0>, 'Solar8000/FEO2': <vitaldb.utils.Track object at 0x7f23c4e94820>, 'Solar8000/FIO2': <vitaldb.utils.Track object at 0x7f23c4e947f0>, 'Solar8000/VENT_INSP_TM': <vitaldb.utils.Track object at 0x7f23c4e94700>, 'Solar8000/VENT_SET_TV': <vitaldb.utils.Track object at 0x7f23c4e946a0>, 'Solar8000/VENT_SET_PCP': <vitaldb.utils.Track object at 0x7f23c4e94670>, 'EVENT': <vitaldb.utils.Track object at 0x7f23c4e95000>}\n",
            "type: 1 (1:wav, 2:num, 5:str)\n",
            "name: ART\n",
            "device name: SNUADC\n",
            "sample rate: 500.0\n",
            "unit: mmHg\n",
            "mindisp: 0.0\n",
            "maxdisp: 200.0\n",
            "color: 4294901760\n",
            "{'dt': 4102444801.7878857, 'val': array([494, 494, 494, 492, 495, 493, 495, 495, 494, 495, 494, 494, 494,\n",
            "       494, 493, 494, 494, 495, 495, 493, 493, 494, 494, 494, 493, 495,\n",
            "       495, 494, 493, 494, 493, 493, 493, 495, 493, 493, 494, 493, 493,\n",
            "       494, 494, 494, 493, 494, 493, 495, 495, 493, 494, 494, 494, 493,\n",
            "       494, 495, 493, 493, 493, 493, 493, 494, 494, 493, 494, 494, 494,\n",
            "       494, 494, 494, 495, 494, 495, 494, 493, 494, 494, 494, 494, 494,\n",
            "       493, 494, 494, 494, 495, 492, 494, 493, 494, 494, 494, 494, 493,\n",
            "       494, 494, 494, 493, 494, 494, 493, 494, 495], dtype=uint16)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkGaO65Fdg9s"
      },
      "source": [
        "### Read samples from the VitalFile object\n",
        "You can read the samples using `to_numpy()` method which is equivalant to `vitaldb.vital_recs()`."
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ensf9C8spPyp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uWxgPWudtFZ",
        "outputId": "d91da5e0-c3c0-417a-edd3-35d928ab8ca3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[         nan,          nan],\n",
              "       [         nan,          nan],\n",
              "       [         nan,          nan],\n",
              "       ...,\n",
              "       [ -0.09797144, -20.65918   ],\n",
              "       [ -0.06834793,  32.663483  ],\n",
              "       [  0.45500422,  -4.8598633 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "vf.to_numpy(['ECG_II','ART'], 1/100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aw1kKSnj-m-"
      },
      "source": [
        "You can get the samples as a pandas DataFrame using `to_pandas()` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vFRJZEhLkPb0",
        "outputId": "fa09a3cf-5c68-47f2-d276-0945822009bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    Time    ECG_II        ART\n",
              "0              2100-01-01 00:00:00+00:00       NaN        NaN\n",
              "1       2100-01-01 00:00:00.010000+00:00       NaN        NaN\n",
              "2       2100-01-01 00:00:00.020000+00:00       NaN        NaN\n",
              "3       2100-01-01 00:00:00.030000+00:00       NaN        NaN\n",
              "4       2100-01-01 00:00:00.040000+00:00       NaN        NaN\n",
              "...                                  ...       ...        ...\n",
              "1154205 2100-01-01 03:12:22.050000+00:00 -0.413958   5.014709\n",
              "1154206 2100-01-01 03:12:22.060000+00:00  0.366133   4.027252\n",
              "1154207 2100-01-01 03:12:22.070000+00:00 -0.097971  -20.65918\n",
              "1154208 2100-01-01 03:12:22.080000+00:00 -0.068348  32.663483\n",
              "1154209 2100-01-01 03:12:22.090000+00:00  0.455004  -4.859863\n",
              "\n",
              "[1154210 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14efd9a7-abbf-475f-8c39-0a060a70bda3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>ECG_II</th>\n",
              "      <th>ART</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2100-01-01 00:00:00+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2100-01-01 00:00:00.010000+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2100-01-01 00:00:00.020000+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2100-01-01 00:00:00.030000+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2100-01-01 00:00:00.040000+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1154205</th>\n",
              "      <td>2100-01-01 03:12:22.050000+00:00</td>\n",
              "      <td>-0.413958</td>\n",
              "      <td>5.014709</td>\n",
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              "    <tr>\n",
              "      <th>1154206</th>\n",
              "      <td>2100-01-01 03:12:22.060000+00:00</td>\n",
              "      <td>0.366133</td>\n",
              "      <td>4.027252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1154207</th>\n",
              "      <td>2100-01-01 03:12:22.070000+00:00</td>\n",
              "      <td>-0.097971</td>\n",
              "      <td>-20.65918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1154208</th>\n",
              "      <td>2100-01-01 03:12:22.080000+00:00</td>\n",
              "      <td>-0.068348</td>\n",
              "      <td>32.663483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1154209</th>\n",
              "      <td>2100-01-01 03:12:22.090000+00:00</td>\n",
              "      <td>0.455004</td>\n",
              "      <td>-4.859863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1154210 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14efd9a7-abbf-475f-8c39-0a060a70bda3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-14efd9a7-abbf-475f-8c39-0a060a70bda3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14efd9a7-abbf-475f-8c39-0a060a70bda3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df = vf.to_pandas(['ECG_II', 'ART'], 1/100, return_datetime=True)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IqvPw8dQt-PW"
      },
      "source": [
        "### Edit VitalFile object\n",
        "You can edit VitalFile object. For example, you can crop it, remove or rename tracks and devices."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uzf2y92uT2j",
        "outputId": "781d5470-27ef-419c-c263-453613e66ddb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original duration 11542.097 seconds 79 tracks\n",
            "cropped duration 7882.097 seconds 79 tracks\n",
            "track removed duration 7882.097 seconds 79 tracks\n"
          ]
        }
      ],
      "source": [
        "# cropping\n",
        "print(f'original duration {vf.dtend - vf.dtstart:.3f} seconds {len(vf.trks)} tracks')\n",
        "vf.crop(vf.get_dt(2100, 1, 1, 1))  # remove samples before 2100 Jan 1, 1AM\n",
        "vf.crop(60)  # remove samples within 60 sec from the beginning\n",
        "print(f'cropped duration {vf.dtend - vf.dtstart:.3f} seconds {len(vf.trks)} tracks')\n",
        "\n",
        "# removing tracks\n",
        "vf.remove_tracks(['ECG_FIVE'])\n",
        "print(f'track removed duration {vf.dtend - vf.dtstart:.3f} seconds {len(vf.trks)} tracks')\n",
        "\n",
        "# renaming tracks\n",
        "vf.rename_tracks({'ECG_II': 'ECG_TWO', 'ECG_V5': 'ECG_FIVE'})\n",
        "vf.rename_device('SNUADC', 'SUPER_NICE_ADC')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogWGHQ5lfZP2"
      },
      "source": [
        "### Save the VitalFile object to vital file\n",
        "To save `VitalFile` object to vital file, call `to_vital()` with the file path to save."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tLbry76e3qr",
        "outputId": "1fa9b31e-9ae2-4eab-fd00-73b01a86ac82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "vf.to_vital('1_copyed.vital')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wKlN4U3miUee"
      },
      "source": [
        "### Save the VitalFile object to csv file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "r9ff2be7lmG1",
        "outputId": "a8dd6d7a-76c3-4a27-ef51-ebefbc129c1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    Time  ECG_II        ART\n",
              "0              2100-01-01 01:01:00+00:00     NaN        NaN\n",
              "1       2100-01-01 01:01:00.010000+00:00     NaN        NaN\n",
              "2       2100-01-01 01:01:00.020000+00:00     NaN        NaN\n",
              "3       2100-01-01 01:01:00.030000+00:00     NaN        NaN\n",
              "4       2100-01-01 01:01:00.040000+00:00     NaN        NaN\n",
              "...                                  ...     ...        ...\n",
              "788205  2100-01-01 03:12:22.050000+00:00     NaN   5.014709\n",
              "788206  2100-01-01 03:12:22.060000+00:00     NaN   4.027252\n",
              "788207  2100-01-01 03:12:22.070000+00:00     NaN -20.659180\n",
              "788208  2100-01-01 03:12:22.080000+00:00     NaN  32.663483\n",
              "788209  2100-01-01 03:12:22.090000+00:00     NaN  -4.859863\n",
              "\n",
              "[788210 rows x 3 columns]"
            ],
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              "\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2100-01-01 01:01:00.020000+00:00</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>2100-01-01 01:01:00.040000+00:00</td>\n",
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              "    <tr>\n",
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              "<p>788210 rows × 3 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-656ab7d0-d902-4e5f-9bdd-11507f0be4eb')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-656ab7d0-d902-4e5f-9bdd-11507f0be4eb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "vf.to_csv('1.csv', ['ECG_II', 'ART'], 1/100, return_datetime=True)\n",
        "\n",
        "import pandas as pd\n",
        "pd.read_csv('1.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TA_SAsyTggDN"
      },
      "source": [
        "### Batch extraction of specific tracks from multiple vital files\n",
        "The code below explains how to extract tracks from several vital files in the specific folder and save them with csv file format."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "4SgDfHe1jphi",
        "outputId": "34ada376-9716-40ca-d6d7-ea71e50d7fbe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "converting:3.vital...done\n",
            "converting:1-2.vital...done\n",
            "converting:1-1.vital...done\n",
            "converting:1.vital...done\n",
            "converting:2.vital...done\n",
            "converting:4.vital...done\n",
            "converting:1_copyed.vital...done\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ECG_II        ART\n",
              "0        0.899360  -7.822235\n",
              "1        0.830238  -8.809692\n",
              "2        0.780865  -7.822235\n",
              "3        0.751241  -7.822235\n",
              "4        0.741367  -8.809692\n",
              "...           ...        ...\n",
              "1154026 -0.413958   5.014710\n",
              "1154027  0.366133   4.027252\n",
              "1154028 -0.097971 -20.659180\n",
              "1154029 -0.068348  32.663483\n",
              "1154030  0.455004  -4.859863\n",
              "\n",
              "[1154031 rows x 2 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>ECG_II</th>\n",
              "      <th>ART</th>\n",
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              "<p>1154031 rows × 2 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "TRACK_NAMES = ['ECG_II', 'ART']\n",
        "for filename in os.listdir('.'):  # iterate files\n",
        "    if filename.endswith('.vital'):\n",
        "        print('converting:' + filename, end='...', flush=True)\n",
        "        vf = vitaldb.VitalFile(filename, TRACK_NAMES)  # read vital file\n",
        "        vf.to_csv(filename + '.csv.gz', TRACK_NAMES, 1/100)  # save as compressed csv\n",
        "        print('done')\n",
        "\n",
        "import pandas as pd\n",
        "pd.read_csv('1.vital.csv.gz')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxPqUQdyoLIQ"
      },
      "source": [
        "### Add event track to VitalFile\n",
        "The code below explains how to add an event track to a specific vital file. It is useful when you need to perform automatic labelling for developing machine learning models."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vf = vitaldb.VitalFile('1.vital')\n",
        "\n",
        "# delete the first half\n",
        "vf.crop((vf.dtstart + vf.dtend) // 2, None)\n",
        "\n",
        "# find the last SET_TV_L records\n",
        "event_recs = []\n",
        "recs = vf.find_track('Primus/SET_TV_L').recs\n",
        "if len(recs):\n",
        "    event_recs.append({'dt': recs[-1]['dt'], 'val': 'Vent Off'})\n",
        "\n",
        "# add event track\n",
        "vf.add_track('EVENT', event_recs, mindisp=0, maxdisp=10)\n",
        "vf.to_vital('1-labelled.vital')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y7whrirYoKzG",
        "outputId": "936bfa15-7302-41bc-ca34-6cfd3ddf89e6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjTvUx8BhH0r"
      },
      "source": [
        "## Platform API\n",
        "You can login, list, and download vital files from the VitalDB platform using the `vitaldb` Platform API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "wD7i3z3LhsVn"
      },
      "outputs": [],
      "source": [
        "# path to save downloaded files\n",
        "import os\n",
        "DOWNLOAD_DIR = \"download\"\n",
        "if not os.path.exists(DOWNLOAD_DIR):\n",
        "    os.mkdir(DOWNLOAD_DIR)\n",
        "\n",
        "if vitaldb.login(id=\"vitaldb_test\", pw=\"vitaldb_test\"):\n",
        "    for file in vitaldb.filelist(None, '2021-10-01'):\n",
        "        print(\"Downloading: \" + file['filename'], end='...', flush=True)\n",
        "        vitaldb.download(file['filename'], DOWNLOAD_DIR + '/' + file['filename'])\n",
        "        print('done')\n",
        "else:\n",
        "    print('login error')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JG9qJHAdlQws"
      },
      "source": [
        "You can read the vital file directly from the server by not specifing the file path to the `vitaldb.download` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPgRJnp8leVo",
        "outputId": "608310e8-cb03-4adf-9e31-491fda69286a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TEST1_211020_142621.vital...78 tracks\n"
          ]
        }
      ],
      "source": [
        "vitaldb.login(id=\"vitaldb_test\", pw=\"vitaldb_test\")\n",
        "filename = vitaldb.filelist()[0]['filename']\n",
        "print(filename, end='...', flush=True)\n",
        "\n",
        "# read the vital file directly from the server (no local file will be created)\n",
        "vf = vitaldb.VitalFile(vitaldb.download(filename))\n",
        "print(f'{len(vf.get_track_names())} tracks')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PSrSBxyTpY7_"
      },
      "source": [
        "### For intranet server\n",
        "For intranet server, you can pass the host name and port number to the `vitaldb.login` function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zHur5QU9jDXV"
      },
      "outputs": [],
      "source": [
        "# the code below will be executed only at SNUH\n",
        "vitaldb.login(id='admin', pw='pass', host='snuh.vitaldb.net', port=80)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oe3WAu3oem38"
      },
      "source": [
        "## Open Dataset API\n",
        "VitalDB python library also include the functions that support read VitalDB open dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36dVA4UMYhOe"
      },
      "source": [
        "### Find cases in the VitalDB open dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5g-6tMedY14-"
      },
      "outputs": [],
      "source": [
        "caseids = vitaldb.find_cases(['ECG_II', 'ART'])\n",
        "len(caseids)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxrktnXaXPQ7"
      },
      "source": [
        "### Read an open dataset case\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zSv9M0h7ZZuE"
      },
      "outputs": [],
      "source": [
        "vals = vitaldb.load_case(caseids[0], ['ECG_II','ART'], 1/100)\n",
        "print(vals)\n",
        "\n",
        "ecg = vals[:,0]\n",
        "art = vals[:,1]\n",
        "\n",
        "# plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.subplot(211)\n",
        "plt.plot(ecg[110000:111000], color='g')\n",
        "plt.subplot(212)\n",
        "plt.plot(art[110000:111000], color='r')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ETIrPMJb4DQ"
      },
      "source": [
        "Same as `load_case` in the functional API, you can download the tracks from the VitalDB open dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "czvq-HfaaXRa"
      },
      "outputs": [],
      "source": [
        "vf = vitaldb.VitalFile(1, ['ECG_II', 'ART'])  # load the first case from open dataset\n",
        "print(f'{len(vf.get_track_names())} tracks')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvWQq9yAc7QN"
      },
      "source": [
        "With the code below, you can download the first 5 cases that have 'Primus/AWP' track in the open dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aOACz9m-caOL"
      },
      "outputs": [],
      "source": [
        "track_names = ['AWP']\n",
        "for caseid in vitaldb.find_cases(track_names)[:5]:\n",
        "    vf = vitaldb.VitalFile(caseid, track_names)\n",
        "    vf.to_vital(f'awp{caseid}.vital')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "vitaldb_python_library.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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